Introduction

The digital economy has generated an unprecedented volume of high-frequency data, offering a powerful new lens for understanding economic trends. High-frequency price data, collected in real-time from a wide array of digital sources, provides a dynamic and granular view of inflation far beyond what traditional, low-frequency surveys can offer. This modern approach enables analysts to detect and respond to subtle price changes with greater speed and precision, offering a significant advantage in a rapidly evolving market. Leveraging this data is essential for accurate economic forecasting and strategic decision-making in both the public and private sectors.

This program provides participants with the skills to harness this vast data stream for advanced inflation analysis. The curriculum is designed to move from foundational principles of data acquisition and cleaning to sophisticated machine learning models and real-time visualization techniques. By the conclusion of the course, participants will be able to build and deploy their own data pipelines, apply cutting-edge analytical methods, and generate actionable insights from high-frequency data. This hands-on training will empower you to become a leader in the next generation of economic and financial analysis.

Duration: 10 Days

Target Audience

• Economic Policymakers
• Financial Market Analysts
• Data Scientists and Engineers
• Quantitative Researchers
• Central Bank Economists
• Risk Management Professionals
• Academics and Researchers
• Business Intelligence Managers
• Consumer Price Index (CPI) Analysts
• Fintech Professionals

Objectives

• Understand the conceptual differences between high-frequency and traditional data.
• Master the techniques for scraping and ingesting real-time price data.
• Apply advanced econometric models for short-term forecasting.
• Develop machine learning models to predict price movements.
• Create and interpret real-time data dashboards.
• Recognize the challenges of data noise and missing values.
• Assess the impact of supply chain and external factors on prices.
• Integrate diverse data streams for a holistic view.
• Communicate complex analytical findings to stakeholders.
• Build a functional system for continuous inflation monitoring.

Course Modules

Module 1: Introduction to High-Frequency Data

• What defines high-frequency economic data.
• Key sources of real-time price information.
• The benefits and challenges of granular data.
• Use cases in inflation tracking and economic analysis.
• Ethical considerations and data privacy.

Module 2: Data Sourcing and Ingestion

• Web scraping techniques for e-commerce sites.
• Using APIs to access data from providers.
• Streaming data ingestion platforms.
• Data governance and ensuring data quality.
• Building a scalable data pipeline.

Module 3: Data Cleaning and Preprocessing

• Handling missing values and outliers in time series.
• Data normalization and feature scaling.
• Resampling and aggregation techniques.
• Identifying and correcting data errors.
• Practical exercises in data wrangling.

Module 4: Econometric Modeling

• Traditional time series models for forecasting.
• Autoregressive and moving average models.
• Vector autoregression (VAR) for multiple variables.
• The use of statistical software for analysis.
• Model diagnostics and validation.

Module 5: Machine Learning for Forecasting

• Introduction to supervised learning for time series.
• Tree-based models like Random Forests and Gradient Boosting.
• Neural networks for complex patterns.
• Ensemble methods for improved accuracy.
• Hyperparameter tuning and model optimization.

Module 6: Natural Language Processing (NLP)

• Analyzing text for economic sentiment.
• Sourcing and processing news articles and reports.
• Building a sentiment dictionary for economic terms.
• Using NLP to identify supply chain issues.
• Extracting structured data from unstructured text.

Module 7: Geospatial Analysis of Price Data

• Mapping price variations across different locations.
• Identifying local market dynamics and outliers.
• Using geographic information systems (GIS).
• Visualizing regional inflation disparities.
• Correlating price data with demographic and geographic factors.

Module 8: Data Visualization

• Principles of effective data storytelling.
• Creating dynamic and interactive dashboards.
• Tools for real-time visualization.
• Highlighting key trends and anomalies.
• Communicating insights clearly and effectively.

Module 9: Cloud Computing for Data Analysis

• Overview of cloud services for big data.
• Storing large datasets efficiently.
• Using cloud-based data processing tools.
• Scaling analysis with distributed computing.
• Security and access management.

Module 10: Case Studies in High-Frequency Data

• Examining real-world applications of price tracking.
• Analysis of consumer spending and mobility data.
• Impact of specific events on inflation.
• Discussing the lessons learned from past projects.
• Group analysis of a contemporary economic event.

Module 11: Building a Real-Time Data Pipeline

• Designing an end-to-end data flow.
• Implementing data ingestion with streaming technologies.
• Creating automated cleaning and processing scripts.
• Setting up automated model retraining.
• Integrating dashboards for continuous monitoring.

Module 12: Ethical Considerations in AI and Data

• Data privacy and consumer consent.
• Avoiding bias in models and data.
• The impact of automated analysis on public policy.
• Ensuring transparency and explainability in models.
• Responsible data governance practices.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 10 working days before commencement of the training.